Vehicle Detection Project¶
In [1]:
# Pipeline for Project 5
# 1. Extract Features from images
# 2. Train Classifier
# 3. Sliding window to make detections in images
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
import pdb
from skimage.feature import hog
from sklearn.cross_validation import train_test_split
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from scipy.ndimage.measurements import label
import time
%matplotlib inline
test_mode = True
In [2]:
# Method for printing images
def print_image(img, title = ""):
plt.figure(figsize=(7, 7))
plt.imshow(img)
plt.title(title)
def print_gray_image(img, title = ""):
plt.figure(figsize=(7, 7))
plt.imshow(img, cmap="gray")
plt.title(title)
In [3]:
# Read in our vehicles and non-vehicles
cars = glob.glob('/Users/Akshay/projects/carnd/CarND-Vehicle-Detection/feature_extraction_data/vehicles/*.jpeg')
notcars = glob.glob('/Users/Akshay/projects/carnd/CarND-Vehicle-Detection/feature_extraction_data/non-vehicles/*.jpeg')
test_images = glob.glob('/Users/Akshay/projects/carnd/CarND-Vehicle-Detection/test_images/*.jpg')
In [4]:
# Define a function to compute binned color features
def bin_spatial(img, size=(32, 32)):
# Use cv2.resize().ravel() to create the feature vector
features = cv2.resize(img, size).ravel()
# Return the feature vector
return features
In [5]:
# Define a function to compute color histogram features
def color_hist(img, nbins=32, bins_range=(0, 256)):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
In [6]:
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
In [7]:
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs,
cspace='RGB',
orient=9,
pix_per_cell=8,
cell_per_block=2,
hog_channel=0,
spatial_size=(32, 32),
hist_bins=32,
hist_range=(0, 256)):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if cspace != 'RGB':
if cspace == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif cspace == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif cspace == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif cspace == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif cspace == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
spatial_features = bin_spatial(feature_image, size = spatial_size)
hist_features = color_hist(feature_image, nbins = hist_bins, bins_range = hist_range)
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
features.append(np.concatenate((spatial_features, hist_features, hog_features)))
# Return list of feature vectors
return features
In [8]:
if test_mode:
# Generate a random index to look at a car image
ind = np.random.randint(0, len(cars))
image = mpimg.imread(cars[ind])
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Define HOG parameters
orient = 9
pix_per_cell = 8
cell_per_block = 2
# Call our function with vis=True to see an image output
hog_features, hog_image = get_hog_features(gray,
orient,
pix_per_cell,
cell_per_block,
vis=True,
feature_vec=False)
print_image(image, 'Orig Image')
print_gray_image(hog_image, 'HOG Visualization')
In [9]:
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9
pix_per_cell = 8
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial = 32
histbin = 16
t = time.time()
car_features = extract_features(cars,
cspace = colorspace,
orient = orient,
pix_per_cell = pix_per_cell,
cell_per_block = cell_per_block,
hog_channel = hog_channel,
spatial_size=(spatial, spatial),
hist_bins=histbin, hist_range=(0, 256))
notcar_features = extract_features(notcars,
cspace = colorspace,
orient = orient,
pix_per_cell = pix_per_cell,
cell_per_block = cell_per_block,
hog_channel = hog_channel,
spatial_size=(spatial, spatial),
hist_bins=histbin, hist_range=(0, 256))
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG features...')
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector. 1 if car present and 0 if not
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.2, random_state=rand_state)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t = time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t = time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these', n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
In [10]:
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img,
x_start_stop=[None, None],
y_start_stop=[None, None],
xy_window=(64, 64),
xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img,
color_space='RGB',
spatial_size=(32, 32),
hist_bins=32,
orient=9,
pix_per_cell=8,
cell_per_block=2,
hog_channel=0,
spatial_feat=True,
hist_feat=True,
hog_feat=True):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
return np.concatenate(img_features)
In [11]:
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
In [24]:
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def uniform_box_size(bbox):
min_side_len = 128
x1 = bbox[0][0]
y1 = bbox[0][1]
x2 = bbox[1][0]
y2 = bbox[1][1]
if (x2 - x1) < min_side_len:
expand_size = int((x2 - x1) / 2) + 10
x1 -= expand_size
x2 += expand_size
if (y2 - y1) < min_side_len:
expand_size = int((y2 - y1) / 2)
y1 -= expand_size
y2 += expand_size
if (x2 - x1) > min_side_len:
contract_size = int((x2 - x1 - min_side_len) / 2) - 10
x1 += contract_size
x2 -= contract_size
if (y2 - y1) > min_side_len:
contract_size = int((y2 - y1 - min_side_len) / 2)
y1 += contract_size
y2 -= contract_size
return ((x1, y1), (x2, y2))
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
bbox = uniform_box_size(bbox)
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
In [25]:
y_start_stop = [384, 640]
x_start_stop = [672, None]
def detect_and_draw(image):
heat = np.zeros_like(image[:,:,0]).astype(np.float)
draw_image = np.copy(image)
draw_image = np.copy(image)
all_windows = []
for scale_mult in [3, 3.5, 4]:
windows = slide_window(image,
x_start_stop = x_start_stop,
y_start_stop = y_start_stop,
xy_window=(int(32* scale_mult), int(32 * scale_mult)),
xy_overlap=(0.85, 0.85))
window_img = draw_boxes(draw_image, windows, color=(255, 0, 0), thick = 6)
if test_mode:
print_image(window_img)
all_windows += windows
hot_windows = search_windows(image,
all_windows,
svc,
X_scaler,
color_space = colorspace,
spatial_size = (spatial, spatial),
hist_bins = histbin,
orient = orient,
pix_per_cell = pix_per_cell,
cell_per_block = cell_per_block,
hog_channel = hog_channel,
spatial_feat = True,
hist_feat = True,
hog_feat = True)
heat = add_heat(heat, hot_windows)
heat = apply_threshold(heat, 2)
heatmap = np.clip(heat, 0, 255)
if test_mode:
print_image(heatmap)
# Find final boxes from heatmap using label function
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(image), labels)
if test_mode:
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick = 4)
print_image(window_img)
return draw_img
test_mode = True
if test_mode == True:
for image in test_images:
print("Running pipeline on image")
image = mpimg.imread(image)
window_img = detect_and_draw(image)
print_image(window_img)
In [19]:
if not test_mode:
output = './p5.mp4'
clip1 = VideoFileClip("test_video.mp4") #.subclip(30, 35)
white_clip = clip1.fl_image(detect_and_draw) # NOTE: this function expects color images!!
%time white_clip.write_videofile(output, audio=False)
In [ ]: